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arXiv · 2609.34271

Scaling Versatile 3D Assets Editing with a Million-Scale Dataset

Badi Li·Tianxin Huang·Yu Zhou·Wei-Shi Zheng·Yi Ma·Shenghua Gao
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Abstract

Although recent 3D generative models produce increasingly realistic assets, controllable 3D asset editing remains challenging. Existing methods are limited by scarce training data, insufficient source-aware modeling, and a lack of practical evaluation protocols. To address these limitations, we present Alchemy3D, a unified framework for training and evaluating versatile 3D asset editors that covers data construction, model architecture, and benchmark evaluation. Specifically, we curate Alchemy3D-1M, a large-scale 3D editing dataset containing 1.25M assets and 1.38M editing pairs across seven editing types. On this data, we train a family of generative flow models for general-purpose 3D asset editing. The model family supports image- and text-conditioned editing, few-step inference, and transfer to multi-view 3D part segmentation. We further introduce GEdit3D-Bench, a large-scale, open-world benchmark with a multi-dimensional evaluation protocol. Across existing and newly introduced benchmarks, our method outperforms prior methods on most metrics of editing fidelity, source preservation, and visual quality.

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Badi Li, Tianxin Huang, Yu Zhou, Wei-Shi Zheng, Yi Ma, Shenghua Gao. 2026-09-28. Scaling Versatile 3D Assets Editing with a Million-Scale Dataset. https://arxiv.org/abs/2609.34271

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